Secure and Private Collaborative Environments for Biomedical Analytics Privacy and confidentiality are critical to healthcare. However, preserving privacy is a non-trivial task because any protection scheme essentially involves a tradeoff with data utility. Furthermore, lack of access to biomedical data can lead to fragmentation of care, resulting in higher economic and social costs, higher safety risk from avoidable drug interactions side-effects, and is a significant impediment to biomedical research. The primary aim of this project is to facilitate biomedical research in collaborative environments by developing technologies for secure and privacy-preserving exploratory analysis. This is critical to the big data to knowledge (BD2K) initiative since it can facilitate biomedical research in collaborative environments. The proposed work addresses two complementary aims. First, we will develop technologies that enable exploratory analysis of data to determine its usability and relevance to the specific biomedical research task. We will also develop technologies to enable the measurement and mitigation of additional privacy/security risk due to accessing this data, thus enabling proper control over the data. The proposed solutions will utilize and enhance the state of the art technological solutions such as privacy-preserving sampling and analysis algorithms, risk-based access control, and query auditing. By doing so, the proposed work will enhance biomedical study design, exploratory data analysis, and hypothesis discovery without compromising on privacy. The project will result in open-source, freely available software tools to perform utility analysis and risk analysis. These will be integrated into REDCap, a data collection and management system used widely for providing translational research informatics support. Several ongoing projects at Rutgers and UCSD will serve as initial pilot customers for the proposed work, with other research groups also being involved at a later stage.